Intelligent Pneumonia Screening: A Convolutional Neural Network Approach Using Chest Radiographs
TL;DR
This study examines a deep learning technique called a Convolutional Neural Network that looks at chest X-rays to identify pneumonia and suggests that CNN-based models have the capacity to aid radiologists in the early diagnosis to ease the medical intervention and minimize diagnostic errors.
Abstract
: Pneumonia continues to be a major source of morbidity and mortality globally, especially in developing countries where a shortage of specialists makes radiological assessment challenging. Patients' survival and appropriate treatment depend on a timely and accurate diagnosis. This study examines a deep learning technique called a Convolutional Neural Network (CNN) that looks at chest X-rays to identify pneumonia. To accurately classify normal and pneumonia-infected lungs, the proposed system makes use of sophisticated picture preprocessing, data augmentation, and improved CNNs to find discriminative spatial characteristics. Using pre-trained models like VGG16, ResNet50, and DenseNet121, it also investigated transfer learning to improve model performance and generalization with little datasets. The CNN model demonstrated 96.8 accuracy, 95.6 precision, 97.3 recall, and 96.4 F1-score on the publicly available Chest X-Ray (Pneumonia) dataset. The results suggest that CNN-based models have the capacity to aid radiologists in the early diagnosis to ease the medical intervention and minimize diagnostic errors. This study is part of the developing body of AI-based healthcare diagnostics, and it highlights the possibility of deep learning in medical image processing and disease prediction.